Deep reinforcement learning extracts the optimal sepsis treatment policy from treatment records.

ABSTRACT:

Background

Sepsis is one of the most life-threatening medical conditions. Therefore, many clinical trials have been conducted to identify optimal treatment strategies for sepsis. However, finding reliable strategies remains challenging due to limited-scale clinical tests. Here we tried to extract the optimal sepsis treatment policy from accumulated treatment records.

Methods

In this study, with our modified deep reinforcement learning algorithm, we stably generated a patient treatment artificial intelligence model. As training data, 16,744 distinct admissions in tertiary hospitals were used and tested with separate datasets. Model performance was tested by t test and visualization of estimated survival rates. We also analyze model behavior using the confusion matrix, importa

SUBMITTER: Choi Y 

PROVIDER: S-EPMC11584651 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature